Abstract ID: 3.11222 | Not reviewed | Requested as: Talk | TBA | TBA

In mountainous regions, forests provide an essential ecosystem service by protecting human lives, infrastructure, and economic activities from natural hazards. Depending on site-specific conditions, forests can reduce the probability and/or intensity of gravitational mass flows (GMFs) such as snow avalanches, shallow landslides, or rockfall, complementing structural and non-structural mitigation measures in disaster risk reduction. The term “protective function” describes both the potential capacity of forests to mitigate natural hazards, as well as the societal demand for this protection. For GMFs, the term “direct object-protective function” emphasizes that the protective function of a forest depends on its spatial relationship with process zones and human assets. For example, a forest with a direct object-protective function against rockfall must be positioned between an upslope release zone and a downslope infrastructure asset. Modeling and mapping forests with a direct object-protective function supports protective forest and natural hazard management and facilitates further analysis, such as socioeconomic assessments of protective forests. In recent years, various methods and modeling approaches have been developed for this purpose and applied in regional to trans-national case studies. In Austria, decades of research and development have culminated in the first national indication map of protective forests, marking a significant step toward objectively assessing forests’ direct object-protective functions in natural hazard management. This contribution outlines the methodology used to model forests with a direct object-protective function in Austria and presents its implementation in open-source tools. We introduce the empirically based GMF runout model com4FlowPy and its “back-tracking” functionality, which automatically identifies process zones upslope of at-risk infrastructure, thus enabling the identification of forests with a direct object-protective function against different GMFs. The implemented algorithms are presented, and input data requirements as well as model results are discussed based on a regional case study. We further review com4FlowPy’s recent integration into the open avalanche framework AvaFrame (avaframe.org) and provide an outlook on ongoing model developments, the potential adoption of the model by researchers and practitioners, and its applications beyond Austria.